TY - JOUR
T1 - Automated analysis of nuclear magnetic resonance assignments for proteins
AU - Zimmerman, Diane E.
AU - Montelione, Gaetano T.
N1 - Funding Information:
We thank Professor Casimir Kulikowsi for helpful discussions and comments on the manuscript. This work wac supported by grants from the National Science Foundation (MC&Y407569 and NSF Young Investigator Award MC%9357526). the National Institutes of Health (GM47014), and by a National Institutes of Health Postdoctoral Fellowship Award (LM00036) to DEZ.
PY - 1995/10
Y1 - 1995/10
N2 - Recent developments in protein NMR technology provide spectral data that are highly amenable to analysis by computer software systems. Automated methods of analysis use constraint satisfaction, pseudoenergy minimization, directed search, neural net, simulated annealing, and/or genetic algorithms to establish sequential links and sequence-specific assignments. The most advanced systems provide automated analysis of complete backbone and extensive side-chain resonance assignments for proteins of 50-150 amino acids.
AB - Recent developments in protein NMR technology provide spectral data that are highly amenable to analysis by computer software systems. Automated methods of analysis use constraint satisfaction, pseudoenergy minimization, directed search, neural net, simulated annealing, and/or genetic algorithms to establish sequential links and sequence-specific assignments. The most advanced systems provide automated analysis of complete backbone and extensive side-chain resonance assignments for proteins of 50-150 amino acids.
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U2 - 10.1016/0959-440X(95)80060-3
DO - 10.1016/0959-440X(95)80060-3
M3 - Article
C2 - 8574703
AN - SCOPUS:0028883616
SN - 0959-440X
VL - 5
SP - 664
EP - 673
JO - Current Opinion in Structural Biology
JF - Current Opinion in Structural Biology
IS - 5
ER -